Survey of Federated Learning Models for Spatial-Temporal Mobility Applications
Yacine Belal,
Sonia Ben Mokhtar,
Hamed Haddadi
et al.
Abstract:Federated learning involves training statistical models over edge devices such as mobile phones such that the training data is kept local. Federated Learning (FL) can serve as an ideal candidate for training spatial temporal models that rely on heterogeneous and potentially massive numbers of participants while preserving the privacy of highly sensitive location data. However, there are unique challenges involved with transitioning existing spatial temporal models to federated learning. In this survey paper, w… Show more
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